Mobility Resilient Vehicular Federated Learning: Enhancing Training Efficiency in Dynamic Environments
Bibliographic record
Abstract
The vehicular environment presents unique challenges, including massive data generation, stringent latency requirements for safety-critical applications, bandwidth limitations, and intermittent connectivity, which make centralized learning approaches impractical. Vehicular Federated Learning (VFL) enables distributed model training by leveraging local data from connected vehicles, while preserving data privacy and reducing network overhead. However, the dynamic nature of VFL presents several additional challenges. High vehicle mobility and unstable channels lead to inconsistent client participation, while heterogeneous vehicle capabilities result in unbalanced training workloads and competitive resource allocation. These challenges significantly degrade VFL model performance and prolong training periods. In this paper, we propose a Mobility Resilient Vehicular Federated Learning (MR-VFL) scheme, which comprises two key components: an amplification-based adaptive vehicular FL (AVFL) training scheme and a dual-timescale FL scheduler. Specifically, AVFL adapts local training epochs to vehicle capabilities to improve scheduling flexibility and alleviate the impact of insufficient local epochs on model updates, which enhances training efficiency and reduces communication competition. The dual-timescale FL scheduler includes a macro scheduling strategy that optimizes long-term VFL performance based on the correlation between convergence speed and model accuracy, and a Mamba-based real-time scheduler that enhances training efficiency and reduces decision latency in massive vehicles scenarios. Extensive simulations show that MR-VFL effectively mitigates performance degradation due to complex vehicle mobility and heterogeneity, and improves training efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".